Chloroquine (antimalaria medication with anti SARS-CoV activity) solubility in supercritical carbon dioxide.

Unfortunately, malaria still remains a major problem in tropical areas, and it takes thousands of lives each year and causes millions of infected cases. Besides, on December 2019, a new virus known as coronavirus appeared, that its rapid prevalence caused the World Health Organization (WHO) to consider it a pandemic. As a potential drug for controlling or treating these two undesired diseases at the cellular level, chloroquine and its derivatives are being investigated, although they possess side effects, which must be reduced for effective and safe treatments. With respect to the importance of this medicine, the current research aimed to calculate the solubility of chloroquine in supercritical carbon dioxide, and evaluated effect of pressure and temperature on the solubility. The pressure varied between 120 and 400 bar, and temperatures between 308 and 338 K were set for the measurements. The experimental results revealed that the solubility of chloroquine lies between 1.64 × 10-5 to 8.92 × 10-4 (mole fraction) with different functionality to temperature and pressure. Although the solubility was indicated to be strong function of pressure and temperature, the effect of temperature was more profound and complicated. A crossover pressure point was found in the solubility measurements, which indicated similar behaviour to an inflection point. For the pressures higher than the crossover point, the temperature indicated direct effect on the solubility of chloroquine. On the other hand, for pressures less than the crossover point, temperature enhancement led to a reduction in the solubility of chloroquine. Moreover, the obtained solubility results were correlated via semi-empirical density-based thermodynamic correlations. Five correlations were studied including: Kumar & Johnston, Mendez-Santiago-Teja, Chrastil, Bartle et al., and Garlapati & Madras. The best performance was obtained for Mendez-Santiago-Teja's correlation in terms of average absolute relative deviation percent (12.0%), while the other examined models showed almost the same performance for prediction of chloroquine solubility.

Spectrophotometric determination of phenol impurity in phenoxyethanol and phenol index of drinking water and municipal wastewater effluent after salting-out assisted liquid phase microextraction (SA-LPME).

In this study, a novel and convenient analytical method based on salting-out-assisted liquid phase microextraction (SA-LPME) has been developed. A spectrophotometric technique was employed to quantify the concentration of phenol in drinking water and treated wastewater, as well as the phenol impurity in 2-phenoxyethanol (PE). To accomplish this, a solution containing dissolved PE was supplemented with 4-aminoantipyrine (4-AAP) and hexacyanoferrate. Subsequently, NaCl was added to induce the formation of a two-phase system, consisting of fine droplets of PE as an extractant phase in the aqueous phase. The resulting red derivative was then extracted into the extractant phase and separated through centrifugation. Finally, the absorbance of the extracted derivative was measured at 520 nm. The Response Surface Methodology (RSM) based on the Box-Behnken Design (BBD) was employed to optimize the influential factors, namely 4-Aminoantipyrine (4-AAP), buffer (pH = 10), hexacyanoferrate, and NaCl. By utilizing the optimal conditions (buffer: 50 µL, 4-AAP (1% w/v): 80 µL, hexacyanoferrate (10% w/v): 65 µL, and NaCl: 0.7 g per 10 mL of the sample), the limit of detection was determined to be 0.7 ng mL-1 and 0.22 µg g-1 for water and PE samples, respectively. The relative standard deviation (RSD) and correlation of determination (r2) obtained fell within the range of 2.4-6.8% and 0.9983-0.9994, respectively. Moreover, an enrichment factor of 65 was achieved for a sample volume of 10 mL. The phenol concentration in two PE samples (PE-1, PE-2), provided by a pharmaceutical company (Pars Sadra Fanavar, Iran), were determined to be 0.83 ± 0.05 µg g-1 and 2.70 ± 0.14 µg g-1, respectively. Additionally, the phenol index in drinking water and treated municipal wastewater was found to be 3.60 ± 1.06 ng mL-1 and 4.60 ± 1.17 ng mL-1, respectively. These mentioned samples were spiked in order to evaluate the potential influence of the matrix. The relative recoveries from PE-1, PE-2 samples, drinking water, and treated municipal wastewater samples were measured as 104.5%, 97.5%, 101.6%, and 107.8%, respectively, indicating no matrix effect.

Efficient green Cr(VI) adsorbent from sorghum waste: Eco-designed functionalized mesoporous silica FDU-12.

This paper presents an eco-design approach to the synthesis of a highly efficient Cr(VI) adsorbent, utilizing a positively charged surface mesoporous FDU-12 material (designated as MI-Cl-FDU-12) for the first time. The MI-Cl-FDU-12 anion-exchange adsorbent was synthesized via a facile one-pot synthesis approach using sodium silicate extracted from sorghum waste as a green silica source, 1-methyl-3-(triethoxysilylpropyl) imidazolium chloride as a functionalization agent, triblock copolymer F127 as a templating or pore-directing agent, trimethyl benzene as a swelling agent, KCl as an additive, and water as a solvent. The synthesis method offers a sustainable and environmentally friendly approach to the production of a so-called "green" adsorbent with a bimodal micro-/mesoporous structure and a high surface area comparable with the previous reports regarding FDU-12 synthesis. MI-Cl-FDU-12 was applied as an anion exchanger for the adsorption of toxic Cr(VI) oxyanions from aqueous media and various kinetic and isotherm models were fitted to experimental data to propose the adsorption behavior of Cr(VI) on the adsorbent. Langmuir model revealed the best fit to the experimental data at four different temperatures, indicating a homogeneous surface site affinity. The theoretical maximum adsorption capacities of the adsorbent were found to be 363.5, 385.5, 409.0, and 416.9 mg g-1 at 298, 303, 308, and 313 K, respectively; at optimal conditions (pH=2, adsorbent dose=3.0 mg, and contact time of 30 min), surpassing that of most previously reported Cr(VI) adsorbents in the literature. A regeneration study revealed that this adsorbent possesses outstanding performance even after six consecutive recycling.

Synthesis of novel adsorbent based on tetrasulfide-functionalized fibrous silica KCC-1 for removal of Hg(II) cations.

Hg(II) has been identified to be one of the extremely toxic heavy metals because of its hazardous effects and this fact that it is even more hazardous to animals than other pollutants such as Ag, Au, Cd, Ni, Pb, Co, Cu, and Zn. Accordingly, for the first time, tetrasulfide-functionalized fibrous silica KCC-1 (TS-KCC-1) spheres were synthesized by a facile, conventional ultrasonic-assisted, sol-gel-hydrothermal preparation approach to adsorb Hg(II) from aqueous solution. Tetrasulfide groups (-S-S-S-S-) were chosen as binding sites due to the strong and effective interaction of mercury ions (Hg(II)) with sulfur atoms. Hg(II) uptake onto TS-KCC-1 in a batch system has been carried out. Isotherm and kinetic results showed a very agreed agreement with Langmuir and pseudo-first-order models, respectively, with a Langmuir maximum uptake capacity of 132.55 mg g-1 (volume of the solution = 20.0 mL; adsorbent dose = 5.0 mg; pH = 5.0; temperature: 198 K; contact time = 40 min; shaking speed = 180 rpm). TS-KCC-1was shown to be a promising functional nanoporous material for the uptake of Hg(II) cations from aqueous media. To the best of our knowledge, there has been no report on the uptake of toxic Hg(II) cations by tetrasulfide-functionalized KCC-1 prepared by a conventional ultrasonic-assisted sol-gel-hydrothermal synthesis method.

Pressure and temperature predictions of Al2O3/water nanofluid flow in a porous pipe for different nanoparticles volume fractions: combination of CFD and ACOFIS.

Artificial intelligence (AI) techniques have illustrated significant roles in finding general patterns of CFD (Computational fluid dynamics) results. This study is conducted to develop combination of the ant colony optimization (ACO) algorithm with the fuzzy inference system (ACOFIS) for learning the CFD results of a physical case study. This binary join of the ACOFIS and CFD was used for pressure and temperature predictions of Al2O3/water nanofluid flow in a heated porous pipe. The intelligence of ACOFIS is investigated for different input numbers and pheromone effects, as the ant colony tuning parameter. The results showed that the intelligence of the ACOFIS could be found for three inputs (x and y nodes coordinates and nanoparticles fraction) and the pheromone effect of 0.1. At the system intelligence, the ACOFIS could predict the pressure and temperature of the nanofluid on any values of the nanoparticles fraction between 0.5 and 2%. Comparing the ANFIS and the ACOFIS, it was shown that both methods could reach the same accuracy in predictions of the nanofluid pressure and temperature. The root mean square error (RMSE) of the ACOFIS (~ 1.3) was a little more than that of the ANFIS (~ 0.03), while the total process time of the ANFIS (~ 213 s) was a bit more than that of the ACOFIS (~ 198 s). The AI algorithms process time (less than 4 min) shows their ability in the reduction of CFD modeling calculations and expenses.

Predicting Air Superficial Velocity of Two-Phase Reactors Using ANFIS and CFD.

In predicting the turbulence property of gas (bubble) flow in the domain of continuous fluid and liquid, the integration of machine learning and computational fluid dynamics (CFD) methods reduces the overall computational time. This combination enables us to see the effective input parameters in the engineering process and the impact of operating conditions on final outputs, such as gas hold-up, heat and mass transfer, and the flow regime (uniform bubble distribution or nonuniform bubble properties). This paper uses the combination of machine learning and single-size calculation of the Eulerian method to estimate the gas flow distribution in the continuous liquid fluid. To present the machine-learning method besides the Eulerian method, an adaptive neuro-fuzzy inference system (ANFIS) is used to train the CFD finding and then estimate the flow based on the machine-learning method. The gas velocity and turbulent eddy dissipation rate are trained throughout the bubble column reactor (BCR) for each CFD node, and the artificial BCR is predicted by the ANFIS method. This smart reactor can represent the artificial CFD of the BCR, resulting in the reduction of expensive numerical simulations. The results showed that the number of inputs could significantly change this method's accuracy, representing the intelligence of method in the learning data set. Additionally, the membership function specifications can impact the accuracy, particularly, when the process is trained with different inputs. The turbulent eddy dissipation rate can also be predicted by the ANFIS method with a similar model pattern for air superficial gas velocity.

Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results.

Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area is at the beginning, and needs sophisticated algorithms to be developed. No studies are available to consider the efficiency of the other trainers like differential evolution (DE) integrating with the FIS for capturing the pattern of the simulation results generated by CFD technique. Besides, the adjustment of the tuning parameters of the artificial intelligence (AI) algorithm for finding the highest level of intelligence is unavailable. The performance of AI algorithms in the meshing process has not been considered yet. Therfore, herein the Al2O3/water nanofluid flow in a porous pipe is simulated by a sophisticated hybrid approach combining mechnsitic model (CFD) and AI. The finite volume method (FVM) is employed as the CFD approach. Also, the differential evolution-based fuzzy inference system (DEFIS) is used for learning the CFD results. The DEFIS learns the nanofluid velocity in the y-direction, as output, and the nodes coordinates (i.e., x, y, and z), as inputs. The intelligence of the DEFIS is assessed by adjusting the methd's variables including input number, population number, and crossover. It was found that the DEFIS intelligence is related to the input number of 3, the crossover of 0.8, and the population number of 120. In addition, the nodes increment from 4833 to 774,468 was done by the DEFIS. The DEFIS predicted the velocity for the new dense mesh without using the CFD data. Finally, all CFD results were covered with the new predictions of the DEFIS.

Separation of copper ions by nanocomposites using adsorption process.

In this research, a novel nanocomposite adsorbent, graphene oxide modified with magnetite nanoparticles and Lauric acid containing ethylenediaminetetraacetic acid (GFLE) has been applied for the eliminate of Cu2+ ions. Adsorption performance was considered as a function of solution pH, Cu2+ ions concentration (C Cu2+), and temperature (T) and contact time (t). The levels of each variable were statistically optimized by Central Composite Design (CCD) and the response surface methodology (RSM) procedure to enhance the yield of system design. In these calculations, Y was measured as the response (the secondary concentration of Cu2+ ions in mg L-1). Highest copper adsorption occurred at time of 105 min, temperature of 40 °C, the initial concentration of 280 mg L-1, and pH = 1. The sorption equilibrium was well demonstrated using the Freundlich isotherm model. The second-order kinetics model suggested that the sorption mechanism might be ion exchange reactions. Thermodynamic factors and activation energy values displayed that the uptake process of Cu2+ ions was spontaneous, feasible, endothermic and physical in nature. Regeneration studies also revealed that GFLE could be consistently reused up to 3 cycles.

Alkali metal doping of black phosphorus monolayer for ultrasensitive capture and detection of nitrogen dioxide.

Black phosphorus nanostructures have recently sparked substantial research interest for the rational development of novel chemosensors and nanodevices. For the first time, the influence of alkali metal doping of black phosphorus monolayer (BP) on its capabilities for nitrogen dioxide (NO2) capture and monitoring is discussed. Four different nanostructures including BP, Li-BP, Na-BP, and K-BP were evaluated; it was found that the adsorption configuration on Li-BP was different from others such that the NO2 molecule preferred a vertical stabilization rather than a parallel configuration with respect to the surface. The efficiency for the detection increased in the sequence of Na-BP < BP < K-BP < Li-BP, with the most significant improvement of + 95.2% in the case of Li doping. The Na-BP demonstrated the most compelling capacity (54 times higher than BP) for NO2 capture and catalysis (- 24.36 kcal/mol at HSE06/TZVP). Furthermore, the K-doped device was appropriate for both nitrogen dioxide adsorption and sensing while also providing the highest work function sensitivity (55.4%), which was much higher than that of BP (10.4%).

Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm.

The heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Molecular separation of ions from aqueous solutions using modified nanocomposites.

Herein, two novel porous polymer matrix nanocomposites were synthesized and used as adsorbents for heavy metal uptake. Methacrylate-modified large mesoporous silica FDU-12 was incorporated in poly(methyl methacrylate) matrix through an in-situ polymerization approach. For another, amine-modified FDU-12 was composited with Nylon 6,6 via a facile solution blending protocol. Various characterization techniques including small-angle X-ray scattering, FTIR spectroscopy, field emission-scanning electron microscopy, transmission electron microscopy, porosimetry, and thermogravimetric analysis have been applied to investigate the physical and chemical properties of the prepared materials. The adsorption of Pb(II) onto the synthesized nanocomposites was studied in a batch system. After study the effect of solution pH, adsorbent amount, contact time, and initial concentration of metal ion on the adsorption process, kinetic studies were also conducted. For both adsorbents, the Langmuir and pseudo-second-order models were found to be the best fit to predict isotherm and kinetics of adsorption. Based on the Langmuir model, maximum adsorption capacities of 105.3 and 109.9 mg g-1 were obtained for methacrylate-modified FDU-12/poly(methyl methacrylate) and amine-modified FDU-12/Nylon 6,6, respectively.

Performance and application analysis of ANFIS artificial intelligence for pressure prediction of nanofluid convective flow in a heated pipe.

Heat transfer augmentation of the nanofluids is still an attractive concept for researchers due to rising demands for designing efficient heat transfer fluids. However, the pressure loss arisen from the suspension of nanoparticles in liquid is known as a drawback for developing such novel fluids. Therefore, prediction of the nanofluid pressure, especially in internal flows, has been focused on studies. Computational fluid dynamics (CFD) is a commonly used approach for such a prediction of fluid flow. The CFD tools are perfect and precise in prediction of the fluid flow parameters. But they might be time-consuming and expensive, especially for complex models such as 3-dimension modeling and turbulent flow. In addition, the CFD could just predict the pressure, and it is disabled for finding the relationship of such variables. This study is intended to show the performance of the artificial intelligence (AI) algorithm as an auxiliary method for cooperation with the CFD. The turbulent flow of Cu/water nanofluid warming up in a pipe is considered as a sample of a physical phenomenon. The AI algorithm learns the CFD results. Then, the relation between the CFD results is discovered by the AI algorithm. For this purpose, the adaptive network-based fuzzy inference system (ANFIS) is adopted as AI tool. The intelligence condition of the ANFIS is checked by benchmarking the CFD results. The paper outcomes indicated that the ANFIS intelligence is met by employing gauss2mf in the model as the membership function and x, y, and z coordinates, the nanoparticle volume fraction, and the temperature as the inputs. The pressure predicted by the ANFIS at this condition is the same as that predicted by the CFD. The artificial intelligence of ANFIS could find the relation of the nanofluid pressure to the nanoparticle fraction and the temperature. The CFD simulation took much more time (90-110 min) than the total time of the learning and the prediction of the ANFIS (369 s). The CFD modeling was done on a workstation computer, while the ANFIS method was run on a normal desktop.

Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors.

To understand impact of input and output parameters during optimization and degree of complexity, in the current study we numerically designed a bubble column reactor with a single sparger in the middle of the reactor. After that, some input and output parameters were selected in the post-processing of the numerical method, and then the machine learning observation started to investigate the level of complexity and impact of each input on output parameters. The adaptive neuro-fuzzy inference system (ANFIS) method was exploited as a machine learning approach to analyze the gas-liquid flow in the reactor. The ANFIS method was used as a machine learning approach to simulate the flow of a 3D (three-dimensional) bubble column reactor. This model was also used to analyze the influence of input and output parameters together. More specifically, by analyzing the degree of membership functions as a function of each input, the level of complexity of gas fraction was investigated as a function of computing nodes (X, Y, and Z directions). The results showed that a higher number of membership functions results in a better understanding of the process and higher model accuracy and prediction capability. X and Y computing nodes have a similar impact on the gas fraction, while Z computing points (height of reactor) have a uniform distribution of membership function across the column. Four membership functions (MFs) in each input parameter are insufficient to predict the gas fraction in the 3D bubble column reactor. However, by adding two membership functions, all features of gas fraction in the 3D reactor can be captured by the machine learning algorithm. Indeed, the degree of MFs was considered as a function of each input parameter and the effective parameter was found based on the impact of MFs on the output.

Study on novel modified large mesoporous silica FDU-12/polymer matrix nanocomposites for adsorption of Pb(II).

In this study, porous methacrylate-modified FDU-12/poly(methyl methacrylate) and amine-modified FDU-12/Nylon 6 nanocomposites were synthesized via a facile solution casting protocol. The physicochemical properties of the prepared materials were studied using various characterization techniques including Fourier transform-infrared spectroscopy, field emission-scanning electron microscopy, transmission electron microscopy, and nitrogen adsorption/desorption. After characterization of the materials, the prepared nanocomposites were applied as novel adsorbents for the removal of Pb(II) from aqueous media. In this regard, the effect of various parameters including solution pH, adsorbent amount, contact time, and initial concentration of Pb(II) on the adsorption process was investigated. To study the mechanism of adsorption, kinetic studies were conducted. The kinetic models of pseudo-first-order, pseudo-second-order, Elovich, and intraparticle diffusion were employed. The results revealed that the adsorption of Pb(II) onto methacrylate-modified FDU-12/poly(methyl methacrylate) and amine-modified FDU-12/Nylon 6 adsorbents followed the pseudo-second-order kinetic model. Also, different isotherms including Langmuir, Freundlich, and Dubinin-Radushkevich were applied to evaluate the equilibrium adsorption data. Langmuir isotherm provided the best fit with the equilibrium data of both adsorbents with maximum adsorption capacities of 99.0 and 94.3 mg g-1 for methacrylate-modified FDU-12/poly(methyl methacrylate) and amine-modified FDU-12/Nylon 6, respectively, for the removal of Pb(II).

Ability of neural network cells in learning teacher motivation scale and prediction of motivation with fuzzy logic system.

We employed a new approach in the field of social sciences or psychological aspects of teaching besides using a very common software package that is Statistical Package for the Social Sciences (SPSS). Artificial intelligence (AI) is a new domain that the methods of its data analysis could provide the researchers with new insights for their research studies and more innovative ways to analyze their data or verify the data with this method. Also, a very significant element in teaching is teacher motivation that is the trigger that pushes the teachers forward, depending on some internal and external factors. In the current study, seven research questions were designed to explore different aspects of teacher motivation, and they were analyzed via SPSS. The current study also compared the results by using an adaptive neuro-fuzzy inference system (ANFIS). Due to the similarity of ANFIS to humans' brain intelligence, the results of the current study could be similar to humans regarding what happens in reality. To do so, the researchers used the validated teacher motivation scale (TMS) and asked participants to fill the questionnaire, and analyzed the results. When the inputs were added to the ANFIS system, the model indicated a high accuracy and prediction capability. The findings also illustrated the importance of the tuning model parameters for the ANFIS method to build up the AI model with a high repeatability level. The differences between the results and conclusions are discussed in detail in the article.

Controlled release evaluation of paracetamol loaded amine functionalized mesoporous silica KCC1 compared to microcrystalline cellulose based tablets.

In the pharmaceutical manufacturing, drug release behavior development is remained as one of the main challenges to improve the drug effectiveness. Recently, more focus has been done on using mesoporous silica materials as drug carriers for prolonged and superior control of drug release in human body. In this study, release behavior of paracetamol is developed using drug-loaded KCC-1-NH2 mesoporous silica, based on direct compaction method for preparation of tablets. The purpose of this study is to investigate the utilizing of pure KCC-1 mesoporous silica (KCC-1) and amino functionalized KCC-1 (KCC-1-NH2) as drug carriers in oral solid dosage formulations compared to common excipient, microcrystalline cellulose (MCC), to improve the control of drug release rate by manipulating surface chemistry of the carrier. Different formulations of KCC-1 and KCC-NH2 are designed to investigate the effect of functionalized mesoporous silica as carrier on drug controlled-release rate. The results displayed the remarkable effect of KCC-1-NH2 on drug controlled-release in comparison with the formulation containing pure KCC-1 and formulation including MCC as reference materials. The pure KCC-1 and KCC-1-NH2 are characterized using different evaluation methods such as FTIR, SEM, TEM and N2 adsorption analysis.

Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm.

In this investigation, differential evolution (DE) algorithm with the fuzzy inference system (FIS) are combined and the DE algorithm is employed in FIS training process. Considered data in this study were extracted from simulation of a 2D two-phase reactor in which gas was sparged from bottom of reactor, and the injected gas velocities were between 0.05 to 0.11 m/s. After doing a couple of training by making some changes in DE parameters and FIS parameters, the greatest percentage of FIS capacity was achieved. By applying the optimized model, the gas phase velocity in x direction inside the reactor was predicted when the injected gas velocity was 0.08 m/s.

Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system.

Computational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

High performance ozone based advanced oxidation processes catalyzed with novel argon plasma treated iron oxyhydroxide hydrate for phenazopyridine degradation.

The present study has focused on the degradation of phenazopyridine (PhP) as an emerging contaminant through catalytic ozonation by novel plasma treated natural limonite (FeOOH·xH2O, NL) under argon atmosphere (PTL/Ar). The physical and chemical characteristics of samples were evaluated with different analyses. The obtained results demonstrated higher surface area for PTL/Ar and negligible change in crystal structure, compared to NL. It was found that the synergistic effect between ozone and PTL/Ar nanocatalyst was led to highest PhP degradation efficiency. The kinetic study confirmed the pseudo-first-order reaction for the PhP degradation processes included adsorption, peroxone and ozonation, catalytic ozonation with NL and PTL/Ar. Long term application (6 cycles) confirmed the high stability of the PTL/Ar. Moreover, different organic and inorganic salts as well as the dissolved ozone concentration demonstrated the predominant role of hydroxyl radicals and superoxide radicals in PhP degradation by catalytic Ozonation using PTL/Ar. The main produced intermediates during PhP oxidation by PTL/Ar catalytic ozonation were identified using LC-(+ESI)-MS technique. Finally, the negligible iron leaching, higher mineralization rate, lower electrical energy consumption and excellent catalytic activity of PTL/Ar samples demonstrate the superior application of non-thermal plasma for treatment of NL.

Experimental and thermodynamic modeling decitabine anti cancer drug solubility in supercritical carbon dioxide.

Design and development of efficient processes for continuous manufacturing of solid dosage oral formulations is of crucial importance for pharmaceutical industry in order to implement the Quality-by-Design paradigm. Supercritical solvent-based manufacturing can be utilized in pharmaceutical processing owing to its inherent operational advantages. However, in order to evaluate the possibility of supercritical processing for a particular medicine, solubility measurement needs to be carried out prior to process design. The current work reports a systematic solubility analysis on decitabine as an anti-cancer medicine. The solvent is supercritical carbon dioxide at different conditions (temperatures and pressures), while gravimetric technique is used to obtain the solubility data for decitabine. The results indicated that the solubility of decitabine varies between 2.84 × 10-05 and 1.07 × 10-03 mol fraction depending on the temperature and pressure. In the experiments, temperature and pressure varied between 308-338 K and 12-40 MPa, respectively. The solubility of decitabine was plotted against temperature and pressure, and it turned out that the solubility had direct relation with the pressure due to the effect of pressure on solvating power of solvent. The effect of temperature on solubility was shown to be dependent on the cross-over pressure. Below the cross-over pressure, there is a reverse relation between temperature and solubility, while a direct relation was observed above the cross-over pressure (16 MPa). Theoretical study was carried out to correlate the solubility data using several thermodynamic-based models. The fitting and model calibration indicated that the examined models were of linear nature and capable to predict the measured decitabine solubilities with the highest average absolute relative deviation percent (AARD %) of 8.9%.